diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml
index e47497b2..86288e2d 100644
--- a/.github/workflows/docs.yaml
+++ b/.github/workflows/docs.yaml
@@ -75,6 +75,8 @@ jobs:
           set -x
           source venv/bin/activate
           cd docs
+          curl -O https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv
+          curl -O https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet
           make html
 
       - name: Copy & push the generated HTML
diff --git a/docs/.gitignore b/docs/.gitignore
index 41e13534..6e8a53b6 100644
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -1,2 +1,4 @@
 pokemon.csv
 yellow_trip_data.parquet
+yellow_tripdata_2021-01.parquet
+
diff --git a/docs/build.sh b/docs/build.sh
index 5afe8581..31398d19 100755
--- a/docs/build.sh
+++ b/docs/build.sh
@@ -19,8 +19,17 @@
 #
 
 set -e
+
+if [ ! -f pokemon.csv ]; then
+    curl -O https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv
+fi
+
+if [ ! -f yellow_tripdata_2021-01.parquet ]; then
+    curl -O https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet
+fi
+
 rm -rf build 2> /dev/null
 rm -rf temp 2> /dev/null
 mkdir temp
 cp -rf source/* temp/
-make SOURCEDIR=`pwd`/temp html
\ No newline at end of file
+make SOURCEDIR=`pwd`/temp html
diff --git a/docs/source/images/jupyter_lab_df_view.png b/docs/source/images/jupyter_lab_df_view.png
new file mode 100644
index 00000000..9dafb4f6
Binary files /dev/null and b/docs/source/images/jupyter_lab_df_view.png differ
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b0103a33..34eb23b2 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -43,27 +43,13 @@ Example
 
 .. ipython:: python
 
-    import datafusion
-    from datafusion import col
-    import pyarrow
+    from datafusion import SessionContext
 
-    # create a context
-    ctx = datafusion.SessionContext()
+    ctx = SessionContext()
 
-    # create a RecordBatch and a new DataFrame from it
-    batch = pyarrow.RecordBatch.from_arrays(
-        [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
-        names=["a", "b"],
-    )
-    df = ctx.create_dataframe([[batch]], name="batch_array")
+    df = ctx.read_csv("pokemon.csv")
 
-    # create a new statement
-    df = df.select(
-        col("a") + col("b"),
-        col("a") - col("b"),
-    )
-
-    df
+    df.show()
 
 
 .. _toc.links:
@@ -85,9 +71,10 @@ Example
 
    user-guide/introduction
    user-guide/basics
-   user-guide/configuration
+   user-guide/data-sources
    user-guide/common-operations/index
    user-guide/io/index
+   user-guide/configuration
    user-guide/sql
 
 
diff --git a/docs/source/user-guide/basics.rst b/docs/source/user-guide/basics.rst
index 3c97d1ef..f37378a4 100644
--- a/docs/source/user-guide/basics.rst
+++ b/docs/source/user-guide/basics.rst
@@ -20,72 +20,76 @@
 Concepts
 ========
 
-In this section, we will cover a basic example to introduce a few key concepts.
+In this section, we will cover a basic example to introduce a few key concepts. We will use the same
+source file as described in the :ref:`Introduction <guide>`, the Pokemon data set.
 
-.. code-block:: python
+.. ipython:: python
 
-    import datafusion
-    from datafusion import col
-    import pyarrow
+    from datafusion import SessionContext, col, lit, functions as f
 
-    # create a context
-    ctx = datafusion.SessionContext()
+    ctx = SessionContext()
 
-    # create a RecordBatch and a new DataFrame from it
-    batch = pyarrow.RecordBatch.from_arrays(
-        [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
-        names=["a", "b"],
-    )
-    df = ctx.create_dataframe([[batch]])
+    df = ctx.read_parquet("yellow_tripdata_2021-01.parquet")
 
-    # create a new statement
     df = df.select(
-        col("a") + col("b"),
-        col("a") - col("b"),
+        "trip_distance",
+        col("total_amount").alias("total"),
+        (f.round(lit(100.0) * col("tip_amount") / col("total_amount"), lit(1))).alias("tip_percent"),
     )
 
-    # execute and collect the first (and only) batch
-    result = df.collect()[0]
+    df.show()
 
-The first statement group:
+Session Context
+---------------
+
+The first statement group creates a :py:class:`~datafusion.context.SessionContext`.
 
 .. code-block:: python
 
     # create a context
     ctx = datafusion.SessionContext()
 
-creates a :py:class:`~datafusion.context.SessionContext`, that is, the main interface for executing queries with DataFusion. It maintains the state
-of the connection between a user and an instance of the DataFusion engine. Additionally it provides the following functionality:
+A Session Context is the main interface for executing queries with DataFusion. It maintains the state
+of the connection between a user and an instance of the DataFusion engine. Additionally it provides
+the following functionality:
 
-- Create a DataFrame from a CSV or Parquet data source.
-- Register a CSV or Parquet data source as a table that can be referenced from a SQL query.
-- Register a custom data source that can be referenced from a SQL query.
+- Create a DataFrame from a data source.
+- Register a data source as a table that can be referenced from a SQL query.
 - Execute a SQL query
 
+DataFrame
+---------
+
 The second statement group creates a :code:`DataFrame`,
 
 .. code-block:: python
 
-    # create a RecordBatch and a new DataFrame from it
-    batch = pyarrow.RecordBatch.from_arrays(
-        [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
-        names=["a", "b"],
-    )
-    df = ctx.create_dataframe([[batch]])
+    # Create a DataFrame from a file
+    df = ctx.read_parquet("yellow_tripdata_2021-01.parquet")
 
 A DataFrame refers to a (logical) set of rows that share the same column names, similar to a `Pandas DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`_.
 DataFrames are typically created by calling a method on :py:class:`~datafusion.context.SessionContext`, such as :code:`read_csv`, and can then be modified by
 calling the transformation methods, such as :py:func:`~datafusion.dataframe.DataFrame.filter`, :py:func:`~datafusion.dataframe.DataFrame.select`, :py:func:`~datafusion.dataframe.DataFrame.aggregate`,
 and :py:func:`~datafusion.dataframe.DataFrame.limit` to build up a query definition.
 
-The third statement uses :code:`Expressions` to build up a query definition.
+Expressions
+-----------
+
+The third statement uses :code:`Expressions` to build up a query definition. You can find
+explanations for what the functions below do in the user documentation for
+:py:func:`~datafusion.col`, :py:func:`~datafusion.lit`, :py:func:`~datafusion.functions.round`,
+and :py:func:`~datafusion.expr.Expr.alias`.
 
 .. code-block:: python
 
     df = df.select(
-        col("a") + col("b"),
-        col("a") - col("b"),
+        "trip_distance",
+        col("total_amount").alias("total"),
+        (f.round(lit(100.0) * col("tip_amount") / col("total_amount"), lit(1))).alias("tip_percent"),
     )
 
-Finally the :py:func:`~datafusion.dataframe.DataFrame.collect` method converts the logical plan represented by the DataFrame into a physical plan and execute it,
-collecting all results into a list of `RecordBatch <https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatch.html>`_.
+Finally the :py:func:`~datafusion.dataframe.DataFrame.show` method converts the logical plan
+represented by the DataFrame into a physical plan and execute it, collecting all results and
+displaying them to the user. It is important to note that DataFusion performs lazy evaluation
+of the DataFrame. Until you call a method such as :py:func:`~datafusion.dataframe.DataFrame.show`
+or :py:func:`~datafusion.dataframe.DataFrame.collect`, DataFusion will not perform the query.
diff --git a/docs/source/user-guide/common-operations/aggregations.rst b/docs/source/user-guide/common-operations/aggregations.rst
index 8fee26a1..e458e5fc 100644
--- a/docs/source/user-guide/common-operations/aggregations.rst
+++ b/docs/source/user-guide/common-operations/aggregations.rst
@@ -26,15 +26,7 @@ to form a single summary value. For performing an aggregation, DataFusion provid
 
 .. ipython:: python
 
-    import urllib.request
-    from datafusion import SessionContext
-    from datafusion import col, lit
-    from datafusion import functions as f
-
-    urllib.request.urlretrieve(
-        "https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv",
-        "pokemon.csv",
-    )
+    from datafusion import SessionContext, col, lit, functions as f
 
     ctx = SessionContext()
     df = ctx.read_csv("pokemon.csv")
diff --git a/docs/source/user-guide/common-operations/functions.rst b/docs/source/user-guide/common-operations/functions.rst
index a0b95c90..8d6a8085 100644
--- a/docs/source/user-guide/common-operations/functions.rst
+++ b/docs/source/user-guide/common-operations/functions.rst
@@ -25,14 +25,8 @@ We'll use the pokemon dataset in the following examples.
 
 .. ipython:: python
 
-    import urllib.request
     from datafusion import SessionContext
 
-    urllib.request.urlretrieve(
-    "https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv",
-    "pokemon.csv",
-    )
-
     ctx = SessionContext()
     ctx.register_csv("pokemon", "pokemon.csv")
     df = ctx.table("pokemon")
diff --git a/docs/source/user-guide/common-operations/index.rst b/docs/source/user-guide/common-operations/index.rst
index b15b04c6..d7c708c2 100644
--- a/docs/source/user-guide/common-operations/index.rst
+++ b/docs/source/user-guide/common-operations/index.rst
@@ -18,6 +18,8 @@
 Common Operations
 =================
 
+The contents of this section are designed to guide a new user through how to use DataFusion.
+
 .. toctree::
    :maxdepth: 2
 
diff --git a/docs/source/user-guide/common-operations/select-and-filter.rst b/docs/source/user-guide/common-operations/select-and-filter.rst
index 07590912..083bcbbd 100644
--- a/docs/source/user-guide/common-operations/select-and-filter.rst
+++ b/docs/source/user-guide/common-operations/select-and-filter.rst
@@ -21,18 +21,15 @@ Column Selections
 Use :py:func:`~datafusion.dataframe.DataFrame.select`  for basic column selection.
 
 DataFusion can work with several file types, to start simple we can use a subset of the 
-`TLC Trip Record Data <https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page>`_
+`TLC Trip Record Data <https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page>`_,
+which you can download `here <https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet>`_.
 
 .. ipython:: python
-    
-    import urllib.request
-    from datafusion import SessionContext
 
-    urllib.request.urlretrieve("https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet",
-                               "yellow_trip_data.parquet")
+    from datafusion import SessionContext
     
     ctx = SessionContext()
-    df = ctx.read_parquet("yellow_trip_data.parquet")
+    df = ctx.read_parquet("yellow_tripdata_2021-01.parquet")
     df.select("trip_distance", "passenger_count")
 
 For mathematical or logical operations use :py:func:`~datafusion.col` to select columns, and give meaningful names to the resulting
diff --git a/docs/source/user-guide/common-operations/windows.rst b/docs/source/user-guide/common-operations/windows.rst
index 60917689..8225d125 100644
--- a/docs/source/user-guide/common-operations/windows.rst
+++ b/docs/source/user-guide/common-operations/windows.rst
@@ -30,16 +30,10 @@ We'll use the pokemon dataset (from Ritchie Vink) in the following examples.
 
 .. ipython:: python
 
-    import urllib.request
     from datafusion import SessionContext
     from datafusion import col
     from datafusion import functions as f
 
-    urllib.request.urlretrieve(
-        "https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv",
-        "pokemon.csv",
-    )
-
     ctx = SessionContext()
     df = ctx.read_csv("pokemon.csv")
 
diff --git a/docs/source/user-guide/data-sources.rst b/docs/source/user-guide/data-sources.rst
new file mode 100644
index 00000000..ba5967c9
--- /dev/null
+++ b/docs/source/user-guide/data-sources.rst
@@ -0,0 +1,187 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements.  See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership.  The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License.  You may obtain a copy of the License at
+
+..   http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied.  See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+.. _user_guide_data_sources:
+
+Data Sources
+============
+
+DataFusion provides a wide variety of ways to get data into a DataFrame to perform operations.
+
+Local file
+----------
+
+DataFusion has the abilty to read from a variety of popular file formats, such as :ref:`Parquet <io_parquet>`,
+:ref:`CSV <io_csv>`, :ref:`JSON <io_json>`, and :ref:`AVRO <io_avro>`.
+
+.. ipython:: python
+
+    from datafusion import SessionContext
+    ctx = SessionContext()
+    df = ctx.read_csv("pokemon.csv")
+    df.show()
+
+Create in-memory
+----------------
+
+Sometimes it can be convenient to create a small DataFrame from a Python list or dictionary object.
+To do this in DataFusion, you can use one of the three functions
+:py:func:`~datafusion.context.SessionContext.from_pydict`,
+:py:func:`~datafusion.context.SessionContext.from_pylist`, or
+:py:func:`~datafusion.context.SessionContext.create_dataframe`.
+
+As their names suggest, ``from_pydict`` and ``from_pylist`` will create DataFrames from Python
+dictionary and list objects, respectively. ``create_dataframe`` assumes you will pass in a list
+of list of `PyArrow Record Batches <https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatch.html>`_.
+
+The following three examples all will create identical DataFrames:
+
+.. ipython:: python
+
+    import pyarrow as pa
+
+    ctx.from_pylist([
+        { "a": 1, "b": 10.0, "c": "alpha" },
+        { "a": 2, "b": 20.0, "c": "beta" },
+        { "a": 3, "b": 30.0, "c": "gamma" },
+    ]).show()
+
+    ctx.from_pydict({
+        "a": [1, 2, 3],
+        "b": [10.0, 20.0, 30.0],
+        "c": ["alpha", "beta", "gamma"],
+    }).show()
+
+    batch = pa.RecordBatch.from_arrays(
+        [
+            pa.array([1, 2, 3]),
+            pa.array([10.0, 20.0, 30.0]),
+            pa.array(["alpha", "beta", "gamma"]),
+        ],
+        names=["a", "b", "c"],
+    )
+
+    ctx.create_dataframe([[batch]]).show()
+
+
+Object Store
+------------
+
+DataFusion has support for multiple storage options in addition to local files.
+The example below requires an appropriate S3 account with access credentials.
+
+Supported Object Stores are
+
+- :py:class:`~datafusion.object_store.AmazonS3`
+- :py:class:`~datafusion.object_store.GoogleCloud`
+- :py:class:`~datafusion.object_store.Http`
+- :py:class:`~datafusion.object_store.LocalFileSystem`
+- :py:class:`~datafusion.object_store.MicrosoftAzure`
+
+.. code-block:: python
+
+    from datafusion.object_store import AmazonS3
+
+    region = "us-east-1"
+    bucket_name = "yellow-trips"
+
+    s3 = AmazonS3(
+        bucket_name=bucket_name,
+        region=region,
+        access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
+        secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
+    )
+
+    path = f"s3://{bucket_name}/"
+    ctx.register_object_store("s3://", s3, None)
+
+    ctx.register_parquet("trips", path)
+
+    ctx.table("trips").show()
+
+Other DataFrame Libraries
+-------------------------
+
+DataFusion can import DataFrames directly from other libraries, such as
+`Polars <https://pola.rs/>`_ and `Pandas <https://pandas.pydata.org/>`_.
+Since DataFusion version 42.0.0, any DataFrame library that supports the Arrow FFI PyCapsule
+interface can be imported to DataFusion using the
+:py:func:`~datafusion.context.SessionContext.from_arrow` function. Older verions of Polars may
+not support the arrow interface. In those cases, you can still import via the
+:py:func:`~datafusion.context.SessionContext.from_polars` function.
+
+.. code-block:: python
+
+    import pandas as pd
+
+    data = { "a": [1, 2, 3], "b": [10.0, 20.0, 30.0], "c": ["alpha", "beta", "gamma"] }
+    pandas_df = pd.DataFrame(data)
+
+    datafusion_df = ctx.from_arrow(pandas_df)
+    datafusion_df.show()
+
+.. code-block:: python
+
+    import polars as pl
+    polars_df = pl.DataFrame(data)
+
+    datafusion_df = ctx.from_arrow(polars_df)
+    datafusion_df.show()
+
+Delta Lake
+----------
+
+DataFusion 43.0.0 and later support the ability to register table providers from sources such
+as Delta Lake. This will require a recent version of
+`deltalake <https://delta-io.github.io/delta-rs/>`_ to provide the required interfaces.
+
+.. code-block:: python
+
+    from deltalake import DeltaTable
+
+    delta_table = DeltaTable("path_to_table")
+    ctx.register_table_provider("my_delta_table", delta_table)
+    df = ctx.table("my_delta_table")
+    df.show()
+
+On older versions of ``deltalake`` (prior to 0.22) you can use the 
+`Arrow DataSet <https://arrow.apache.org/docs/python/generated/pyarrow.dataset.Dataset.html>`_
+interface to import to DataFusion, but this does not support features such as filter push down
+which can lead to a significant performance difference.
+
+.. code-block:: python
+
+    from deltalake import DeltaTable
+
+    delta_table = DeltaTable("path_to_table")
+    ctx.register_dataset("my_delta_table", delta_table.to_pyarrow_dataset())
+    df = ctx.table("my_delta_table")
+    df.show()
+
+Iceberg
+-------
+
+Coming soon!
+
+Custom Table Provider
+---------------------
+
+You can implement a custom Data Provider in Rust and expose it to DataFusion through the
+the interface as describe in the :ref:`Custom Table Provider <io_custom_table_provider>`
+section. This is an advanced topic, but a
+`user example <https://github.com/apache/datafusion-python/tree/main/examples/ffi-table-provider>`_
+is provided in the DataFusion repository.
diff --git a/docs/source/user-guide/introduction.rst b/docs/source/user-guide/introduction.rst
index 8abb9113..7b30ef2b 100644
--- a/docs/source/user-guide/introduction.rst
+++ b/docs/source/user-guide/introduction.rst
@@ -39,5 +39,39 @@ You can verify the installation by running:
     import datafusion
     datafusion.__version__
 
+In this documentation we will also show some examples for how DataFusion integrates
+with Jupyter notebooks. To install and start a Jupyter labs session use
 
+.. code-block:: shell
+
+    pip install jupyterlab
+    jupyter lab
+
+To demonstrate working with DataFusion, we need a data source. Later in the tutorial we will show
+options for data sources. For our first example, we demonstrate using a Pokemon dataset that you
+can download
+`here <https://gist.githubusercontent.com/ritchie46/cac6b337ea52281aa23c049250a4ff03/raw/89a957ff3919d90e6ef2d34235e6bf22304f3366/pokemon.csv>`_.
+
+With that file in place you can use the following python example to view the DataFrame in
+DataFusion.
+
+.. ipython:: python
+
+    from datafusion import SessionContext
+
+    ctx = SessionContext()
+
+    df = ctx.read_csv("pokemon.csv")
+
+    df.show()
+
+If you are working in a Jupyter notebook, you can also use the following to give you a table
+display that may be easier to read.
+
+.. code-block:: shell
+
+    display(df)
 
+.. image:: ../images/jupyter_lab_df_view.png
+    :width: 800
+    :alt: Rendered table showing Pokemon DataFrame
diff --git a/docs/source/user-guide/io/avro.rst b/docs/source/user-guide/io/avro.rst
index 5f1ff728..66398ac7 100644
--- a/docs/source/user-guide/io/avro.rst
+++ b/docs/source/user-guide/io/avro.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_avro:
+
 Avro
 ====
 
diff --git a/docs/source/user-guide/io/csv.rst b/docs/source/user-guide/io/csv.rst
index d2a62bfe..144b6615 100644
--- a/docs/source/user-guide/io/csv.rst
+++ b/docs/source/user-guide/io/csv.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_csv:
+
 CSV
 ===
 
diff --git a/docs/source/user-guide/io/json.rst b/docs/source/user-guide/io/json.rst
index f9da3755..39030db7 100644
--- a/docs/source/user-guide/io/json.rst
+++ b/docs/source/user-guide/io/json.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_json:
+
 JSON
 ====
 `JSON <https://www.json.org/json-en.html>`_ (JavaScript Object Notation) is a lightweight data-interchange format.
diff --git a/docs/source/user-guide/io/parquet.rst b/docs/source/user-guide/io/parquet.rst
index 75bc981c..c5b9ca3d 100644
--- a/docs/source/user-guide/io/parquet.rst
+++ b/docs/source/user-guide/io/parquet.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_parquet:
+
 Parquet
 =======
 
@@ -22,7 +24,6 @@ It is quite simple to read a parquet file using the :py:func:`~datafusion.contex
 
 .. code-block:: python
 
-
     from datafusion import SessionContext
 
     ctx = SessionContext()
diff --git a/docs/source/user-guide/io/table_provider.rst b/docs/source/user-guide/io/table_provider.rst
index 2ff9ae46..bd1d6b80 100644
--- a/docs/source/user-guide/io/table_provider.rst
+++ b/docs/source/user-guide/io/table_provider.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_custom_table_provider:
+
 Custom Table Provider
 =====================
 
diff --git a/python/datafusion/dataframe.py b/python/datafusion/dataframe.py
index efd4038a..e283f590 100644
--- a/python/datafusion/dataframe.py
+++ b/python/datafusion/dataframe.py
@@ -473,7 +473,7 @@ def join_on(
         *on_exprs: Expr,
         how: Literal["inner", "left", "right", "full", "semi", "anti"] = "inner",
     ) -> DataFrame:
-        """Join two :py:class:`DataFrame`using the specified expressions.
+        """Join two :py:class:`DataFrame` using the specified expressions.
 
         On expressions are used to support in-equality predicates. Equality
         predicates are correctly optimized
diff --git a/python/datafusion/plan.py b/python/datafusion/plan.py
index 3836edec..a71965f4 100644
--- a/python/datafusion/plan.py
+++ b/python/datafusion/plan.py
@@ -42,7 +42,7 @@ class LogicalPlan:
     (table) with a potentially different schema. Plans form a dataflow tree
     where data flows from leaves up to the root to produce the query result.
 
-    `LogicalPlan`s can be created by the SQL query planner, the DataFrame API,
+    A `LogicalPlan` can be created by the SQL query planner, the DataFrame API,
     or programmatically (for example custom query languages).
     """
 
@@ -107,7 +107,7 @@ def __init__(self, plan: df_internal.ExecutionPlan) -> None:
         self._raw_plan = plan
 
     def children(self) -> List[ExecutionPlan]:
-        """Get a list of children `ExecutionPlan`s that act as inputs to this plan.
+        """Get a list of children `ExecutionPlan` that act as inputs to this plan.
 
         The returned list will be empty for leaf nodes such as scans, will contain a
         single value for unary nodes, or two values for binary nodes (such as joins).